phi-3.5-moe-instruct vs Qwen3-30B-A3B-Instruct-2507
Compare benchmark score, parameter size, model family, and practical tradeoffs between these two Hugging Face LLM models.
Qwen/Qwen3-30B-A3B-Instruct-2507
Metric Comparison
The table keeps the core specs visible for quick evaluation.
| Metric | phi-3.5-moe-instruct | Qwen3-30B-A3B-Instruct-2507 | Difference |
|---|---|---|---|
| Benchmark average score | 98.50 | -98.5 (-%) | |
| Parameter size | N/A | 30.00B | N/A |
| Model family | Microsoft | Qwen | Different |
Performance Verdict
Based on the available leaderboard data, Qwen/Qwen3-30B-A3B-Instruct-2507 has the stronger overall benchmark score.
- Qwen/Qwen3-30B-A3B-Instruct-2507 is the stronger performer, scoring 98.50 on average compared to microsoft/phi-3.5-moe-instruct's .
- Parameter size comparison is not available due to missing parameter metadata.
Integration & Implementation Guide
Learn how to load and execute these models programmatically in Python, JavaScript/TypeScript, Go, Rust, C++, and PHP.
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-3.5-moe-instruct")
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-3.5-moe-instruct")
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-30B-A3B-Instruct-2507")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-30B-A3B-Instruct-2507")
Compare Alternative Models
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